Finance: Interview with Tamara Dull (SAS)

The Best Analytics is at the Edge

By Miran Varga, November, 2016.

Companies of all sizes are mesmerized by the quantity and pace of data in their environments. The experts are refereing to these as big and fast data. What these trends mean for business analytics?

That old precept of what has happened, what’s happening now, and what will happen is being upgraded. The trend of analytics at the edge means both companies and individuals need to absorb and process data faster than ever before. While there is way more data than before the faster generated data also becomes obsolete rapidly. But the results do not only inspire, they might even amaze.Today businesses are literally shocked about the amount and importance of data. What does this flood of data mean to business analytics?

Despite the spectacular progress which we have
witnessed in the last decade business analytics is not almighty. However we are already very close to this ideal.

How do analysts distinguish between good and bad data?

It all depends on the user. It is like with beauty, good data is in the eye of the beholder. Good data is something that can be employed to answer a businesses question or solve a business problem. If it can’t do that, it’s not good.

It looks like modern analytic tools can do just about anything. Is there something they cannot do and will not be able to do for the foreseeable future?

Analytics can never replace the innate human gift of creativity.

Analytic tools also demand certain skills. Data scientists are being sought all over the place but they are in short supply. What defines a data scientist?

The definition of a data scientist has become diluted. At the end of the day, a good data scientist is someone who solves a problem that’s been identified in the company. Some data scientists are statisticians that understand how to prep and use data. Some data scientists are business analysts who can talk about data in the context of its use. Other data scientists are great storytellers who actually paint a picture of the value of data in the business, and with customers and partners, and even beyond. Still others can use data to help to define new business models and support strategies. If companies are going to formalize the role of the data scientist, they need to define it. Every company’s definition is going to be different.

What do you consider as the weakest link of modern analytics?

The weakest link of modern analytics is not an analytics issue. The weakest link of modern analytics is that demand is fast outstripping supply. Analytics skills are in higher demand, the business need for analytics is more urgent, and companies need to apply more rigor and deliberation around prioritizing their analytics capabilities.

How can companies fix this?

The companies must ask themselves what is it they really need and set priorities on how to build and use analytic capabilities. And stick to this plan.

Can the use of business analytics also be dangerous?

If you feed the tools with bad data the results will be bad as well. But in general it is the other way around. The dangers are in the underuse of analytics. Companies that don’t use analytics to understand and improve operations find themselves at a strategic and practical disadvantage.

What will it take in order to fully merge BI and BA with business?

A lot is spoken and written about building a culture of analytics. It really is that important. Executives need to be willing to support formal analytics programs at the enterprise level. They need to endorse and measure the use of analytics; in other words, making it more formal and more systemic in the organization.

How big of a part does analytics play when it comes to digital transformation?

Analytics is part of the fiber of digital, and vice versa. When people talk about digital, they talk about the SMAC – Social, Mobile, Analytics, Cloud – stack. The absence of any one of those components in a digital transformation makes the entire effort error-prone. Moreover, the whole is greater than the sum of its parts because those four components are each critical in their own right, as well as serving the larger digital effort.